Reliability assessment of variational quantum neural networks under noise models and error-mitigation strategies in the NISQ regime

Key Points

• The assessment reveals that noise can significantly affect variational quantum neural networks' reliability, especially in the NISQ regime.
• Key evidence indicates that specific error-mitigation strategies can improve performance metrics, with significant variability observed across different noise models.
• The method involves analyzing several noise models to evaluate the reliability of different quantum neural network configurations within the NISQ framework.
• This analysis highlights the need for robust error-mitigation techniques to enhance quantum computation, with implications for future research and applications.